CN106886839B - Hybrid integer programming-based water-fire-electricity generator set combination optimization scheduling method - Google Patents

Hybrid integer programming-based water-fire-electricity generator set combination optimization scheduling method Download PDF

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CN106886839B
CN106886839B CN201710099977.7A CN201710099977A CN106886839B CN 106886839 B CN106886839 B CN 106886839B CN 201710099977 A CN201710099977 A CN 201710099977A CN 106886839 B CN106886839 B CN 106886839B
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梅生伟
郑天文
陈来军
李春来
杨立滨
魏韡
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Electric Power Research Institute of State Grid Qinghai Electric Power Co Ltd
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Abstract

The invention discloses a combined optimal scheduling method of a water-fire-electricity generator set based on mixed integer programming, which comprises the following steps: acquiring basic data of a single hydropower station, wherein the basic data comprises reservoir water quantity, a limit value of the reservoir water quantity and a limit value of power generation flow; carrying out piecewise linearization on a nonlinear constraint condition in the constraint condition of the single hydropower station optimization scheduling model; solving the single hydropower station optimized dispatching model which enables the objective function F to be minimum according to the constraint condition after the piecewise linearization, the linear constraint condition of the single hydropower station optimized dispatching model and the basic data of the single hydropower station; and determining a combined optimal scheduling model of the water, fire and electricity generating set according to the single hydropower station optimal scheduling model which enables the objective function F to be minimum. The invention avoids directly solving the non-convex nonlinear problem of nonlinear coupling of variables in time and space, and improves the operating efficiency of the power system.

Description

Hybrid integer programming-based water-fire-electricity generator set combination optimization scheduling method
Technical Field
The invention relates to the technical field of optimal scheduling of power systems, in particular to a combined optimal scheduling method of a water-gas-electric generator set based on mixed integer programming.
Background
Along with the rapid development of economy in China, the dual problems of energy shortage and environmental pollution are increasingly highlighted and cannot be ignored. While energy conservation and emission reduction are achieved, new energy sources including hydropower, wind power, solar energy and the like are vigorously developed in China. The access of large-scale clean energy has important significance for reducing carbon dioxide emission, protecting the environment and realizing sustainable development of China. The proportion of the hydropower serving as the installed capacity of various new energy power generation is the highest, and the method has practical research significance. With the vigorous development of cascade hydropower stations in extra-large hydro-junction engineering of three gorges in the Yangtze river, cloud and precious places and the like, the total average installed capacity of hydropower reaches up to million kilowatts, and a huge hydroelectric system has a large optimization margin in time and space, and if the hydropower systems are reasonably utilized, strong social benefits, environmental benefits and economic benefits can be obtained. Under the current hydropower planning system dispatching mode in China, the flexible utilization of hydropower still has defects. Through the fine modeling of the water, fire and electricity optimized dispatching, the flexibility in the water and electricity dispatching can be fully excavated, and the method has a good application prospect.
The difficulty of the optimal scheduling method for the hydroelectric power is that the multiple coupling relation of multivariable in time and space causes the whole problem to become a large-scale non-convex non-linear problem. Secondly, if the problems of the limitation of natural conditions, the uncertainty of incoming water and the like are considered, the difficulty of the whole hydropower optimization scheduling is further increased. Thirdly, the existing method is not accurate enough or is too time-consuming in a large-scale system. Therefore, how to provide a water-fire-electricity optimized scheduling method to overcome the above difficulties becomes a problem to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to provide a hybrid integer programming-based combined optimal scheduling method for a water-fire-electricity generator set, so that the problem of non-convex nonlinearity of nonlinear coupling of variables in time and space is avoided being solved directly, and the operating efficiency of a power system is improved.
In order to achieve the purpose, the invention provides a water-fire-electricity generating set combined optimal scheduling method based on mixed integer programming, which comprises the following steps:
acquiring basic data of a single hydropower station, wherein the basic data comprises reservoir water quantity, a limiting value of the reservoir water quantity and a limiting value of power generation flow;
nonlinear constraint condition in constraint condition of optimal scheduling model of single hydropower station
Figure BDA0001231441200000021
And pkt＝9.8hktqktCarrying out piecewise linearization;
wherein k is the kth reservoir, t is time, formula
Figure BDA0001231441200000022
Indicating downstream water level hdktAnd the generated current qktRelation between Bk0、Bk1、Bk2And Bk3Is a constant; formula pkt＝9.8hktqktRepresenting the generated power p of a hydroelectric power stationktAnd the generated current qktWater head hktThe relationship between;
solving the single hydropower station optimized dispatching model which minimizes an objective function F according to the constraint condition after the piecewise linearization, the linear constraint condition of the single hydropower station optimized dispatching model and the basic data of the single hydropower station; the objective function F is the minimum operation cost or the minimum energy consumption of the thermal power generating unit and the hydroelectric generating unit;
the linear constraint conditions of the single hydropower station optimized dispatching model are as follows:
Figure BDA0001231441200000023
Figure BDA0001231441200000024
Figure BDA0001231441200000025
Figure BDA0001231441200000026
wherein k is the kth reservoir, t is time, formula Vk(t-1)-Vkt＝-Ikt+qktRepresenting the real-time balance of the water volume of the reservoir, i.e. the water volume V of the reservoir at time t-1k(t-1)Water volume V of reservoir at time tktThe difference is equal to the inflow IktAnd the generated current qktThe difference between the difference of the two phases,
Figure BDA0001231441200000027
the minimum value of the water amount of the reservoir is represented,
Figure BDA0001231441200000028
the maximum value of the water amount of the reservoir is represented,
Figure BDA0001231441200000029
the minimum value of the flow rate is shown,
Figure BDA00012314412000000210
the maximum flow is shown; formula (II)
Figure BDA00012314412000000211
Indicating upstream water level huktWater volume V of reservoirk(t-1)、VktRelation of (A)k0And Ak1Is a constant; formula hkt＝hukt-hdktIndicating head hktEqual to the upstream water level huktAnd downstream water level hdktThe difference between the two;
determining a model for the combined optimal scheduling of the water-gas-electric generator set according to the single hydropower station optimal scheduling model which enables the objective function F to be minimum;
and scheduling the water-fire-electric generator set combination according to the model for optimizing the scheduling of the water-fire-electric generator set combination.
Optionally, the objective function F is
F＝min cTx+∑t∑idi(pit)+bTy
The method comprises the following steps that an objective function F is the minimum running cost of a thermal power generating unit and a hydroelectric generating unit, x is a decision variable of the starting and stopping state of the thermal power generating unit, y is a decision variable of the starting and stopping state of the hydroelectric generating unit, c is a starting and stopping price coefficient of the thermal power generating unit, b is a starting and stopping price coefficient of the hydroelectric generating unit, T represents a matrix transpose, diIs the running price coefficient, p, of the ith thermal power generating unititIs the output power, p, of the ith thermal power generating unitjtIs the output power of the jth hydroelectric generating set, pqtIs the q-th demand side load power,
the constraint conditions of the combined optimization scheduling model of the water-fire-electricity generator set are as follows:
Figure BDA0001231441200000031
Figure BDA0001231441200000032
Figure BDA0001231441200000033
Figure BDA0001231441200000034
wherein NT is the number of thermal power generating units, NH is the number of hydroelectric generating units, and chi is the feasibility of the combined scheduling problem of the thermal power generating unitsGamma is a feasible set of the combined scheduling problem of the hydroelectric generating set, and omega (x) is a feasible set of the economic scheduling of the thermal generating set and is constrained by the capacity of the generator and the climbing output; psi (y) is a feasible set for economic dispatch of hydroelectric generating sets, pi is a scale factor of injection power, FlIs the maximum allowed transmission power of line l, equation ∑ipit+∑jpjt＝∑qpqtFor the electrical balance of the hair, formula-Fl≤∑iπilpit+∑jπjlpjt+∑qπqlpqt≤FlIs the limitation of the upper limit and the lower limit of the transmission power capacity.
Optionally, the method further includes:
taking the output flow of the upper-level single hydropower station optimized scheduling model as the input flow of the current-level single hydropower station optimized scheduling model, and constructing a cascade hydropower station optimized scheduling model;
determining the constraint condition of the cascade hydropower station optimized dispatching model as the constraint condition of the single hydropower station optimized dispatching model of each stage;
carrying out piecewise linearization on the nonlinear constraint condition;
and solving the combined optimized dispatching model of the water-gas-electric generator set for minimizing the objective function F according to the constraint conditions and the constraint conditions after the piecewise linearization.
Optionally, the constraint on the nonlinearity is
Figure BDA0001231441200000041
Performing piecewise linearization, specifically comprising:
in the curve
Figure BDA0001231441200000042
Taking n points, and dividing the curve
Figure BDA0001231441200000043
Dividing the sub-field into n-1 sub-fields;
calculating function values corresponding to the n points;
determining a subsection where an independent variable is located, and determining function values of two end points of the subsection where the independent variable is located;
and determining the function value of the independent variable through the function values of the two endpoints, wherein the function value of the independent variable is a weighted average of the function values of the two endpoints.
Optionally, the constraint on the nonlinearity is represented by the formula pkt＝9.8hktqktPerforming piecewise linearization, specifically comprising:
varying the water head hktThe calculation formula of (a) is substituted to obtain a relation having two polynomials, that is,
Figure 1
respectively to two polynomials
Figure BDA0001231441200000045
And
Figure 2
respectively carrying out piecewise linearization;
taking n points on the curves of the two polynomials, and dividing each curve into n-1 subsections;
calculating function values corresponding to the n points;
determining a subsection where an independent variable is located, and determining function values of two end points of the subsection where the independent variable is located;
and determining the function value of the independent variable through the function values of the two endpoints, wherein the function value of the independent variable is a weighted average of the function values of the two endpoints.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: according to the hybrid integer programming-based combined optimal scheduling method for the hydroelectric generating set, decoupling and piecewise linearization are carried out on hydropower-related nonlinear constraint conditions, the problem that a non-convex nonlinear problem of nonlinear coupling of variables in time and space is solved directly is avoided, and the calculation accuracy can be improved on the premise of ensuring higher efficiency. In addition, in the aspect of processing of hydropower nonlinear constraint, compared with a traditional method such as a method of assuming a constant head and then performing piecewise linearization, the method introduces a large error in the assumption of the constant head, if the output curve of the hydroelectric generating set is divided into a series of curve families related to the size of the reservoir capacity, each curve is processed by adopting the piecewise linearization method, and a variable of 0-1 is introduced to determine which curve is suitable for the current situation; the method introduces a large number of discrete variables and reduces the calculation efficiency, and compared with the method of assuming the constant water head, the method of decoupling and piecewise linearization of the hydropower-related nonlinear constraint condition has the advantages of optimizing the total cost, calculating time and calculating precision. The hydropower optimization scheduling model provided by the invention is a universal model, can be applied to a series of scheduling problems related to hydropower in an electric power system, combines the improved hydropower optimization scheduling model and a thermal power unit for combined scheduling, can effectively improve the operation efficiency of the electric power system, reduces the operation cost, and has practical significance.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a hybrid integer programming-based combined optimal scheduling method for a hydro-thermal power generation unit.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a hybrid integer programming-based combined optimal scheduling method for a water-fire-electricity generator set, so that the problem of non-convex nonlinearity of nonlinear coupling of variables in time and space is avoided being solved directly, and the operating efficiency of a power system is improved.
In the prior art, due to the multi-variable multi-coupling relation of the optimal scheduling method of the hydroelectric power in time and space, the whole problem becomes a large-scale non-convex non-linear problem, and if the problems of the limitation of natural conditions, the uncertainty of incoming water and the like are considered, the difficulty of the optimal scheduling of the whole hydroelectric power is further increased. Under the condition of assuming that a water head is constant, the existing scholars carry out piecewise linearization on the output curve of water and electricity, so that the model is converted into a Mixed Integer Programming problem (MIP) to be solved; however, this method has a problem in that the assumption of a constant head introduces a large error. If the output curve of the hydroelectric generating set is divided into a series of curve families related to the size of the reservoir capacity, each curve is processed by adopting a piecewise linearization method, and a variable of 0-1 is introduced to determine which curve is suitable for the current situation; this method introduces a large number of discrete variables, reducing computational efficiency. Therefore, in the application to large power system problems, the traditional hydropower optimization scheduling model still has room for improvement in comparison.
Aiming at the problem of water, fire and electricity optimized dispatching of a large-scale power system, the invention can improve the calculation precision on the premise of ensuring higher efficiency by decoupling and piecewise linearization of water and electricity related nonlinear constraints. Meanwhile, the concept of optimization at different time periods is provided by combining the actual hydropower station dispatching mode in China, and the method is more in line with physical significance. Firstly, modeling a single hydropower station to obtain an optimal scheduling model of the single hydropower station; and then expanding the model to obtain a cascade hydropower dispatching model. The linearization method provided by the invention has advantages in the aspects of optimization cost, calculation time, accuracy and the like.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the method for optimizing and scheduling a water-gas-electric generator set combination based on mixed integer programming provided by the invention comprises the following steps:
step 101: acquiring basic data of a single hydropower station, wherein the basic data comprises reservoir water quantity, a limiting value of the reservoir water quantity and a limiting value of power generation flow;
step 102: nonlinear constraint condition in constraint condition of optimal scheduling model of single hydropower station
Figure BDA0001231441200000061
And pkt＝9.8hktqktCarrying out piecewise linearization;
wherein k is the kth reservoir, t is time, formula
Figure BDA0001231441200000062
Indicating downstream water level hdktAnd the generated current qktRelation between Bk0、Bk1、Bk2And Bk3Is a constant; formula pkt＝9.8hktqktRepresenting the generated power p of a hydroelectric power stationktAnd the generated current qktWater head hktThe relationship between;
specifically, the relationship between the downstream water level and the generated power flow rate can be expressed as a cubic function, that is:
Figure BDA0001231441200000071
wherein, hdtIs the downstream water level, QtFor the flow rate, Qt＝qt+∑zjtSU+stWherein q istThe power generation flow is obtained; z is a radical ofjtSU is the water consumption of power generation and the start-stop state z of the hydroelectric generating setjtCorrelation; stThe overflow amount is the water consumption z caused by power generationjtSU phase comparison power generation flow qktSmall but overflow stOnly during flood periods, and therefore, the outflow Qkt＝qktDownstream water level hdktAnd the generated current qktThe relationship between is
Figure BDA0001231441200000072
Thus, the generated power p of the hydropower station can also be obtainedktAnd the generated current qktWater head hktThe relationship between is pkt＝9.8hktqkt。
The relationship between the variables is determined without considering the power generation water consumption and the overflow amount, and if the power generation water consumption and the overflow amount are considered, the piecewise linearization method is still feasible.
Step 103: solving the single hydropower station optimized dispatching model which enables the objective function F to be minimum according to the constraint condition after the piecewise linearization, the linear constraint condition of the single hydropower station optimized dispatching model and the basic data of the single hydropower station; the objective function F is the minimum operation cost or the minimum energy consumption of the thermal power generating unit and the hydroelectric generating unit;
the linear constraint conditions of the single hydropower station optimized dispatching model are as follows:
Figure BDA0001231441200000073
Figure BDA0001231441200000074
Figure BDA0001231441200000075
Figure BDA0001231441200000076
wherein k is the kth reservoir, t is time, formula Vk(t-1)-Vkt＝-Ikt+qktRepresenting the real-time balance of the water volume of the reservoir, i.e. the water volume V of the reservoir at time t-1k(t-1)Water volume V of reservoir at time tktThe difference is equal to the inflow IktAnd the flow rate QktThe difference between the difference of the two phases,
Figure BDA0001231441200000077
the minimum value of the water amount of the reservoir is represented,
Figure BDA0001231441200000078
the maximum value of the water amount of the reservoir is represented,
Figure BDA0001231441200000079
the minimum value of the flow rate is shown,
Figure BDA00012314412000000710
the maximum flow is shown; formula (II)
Figure BDA00012314412000000711
Indicating upstream water level huktWater volume V of reservoirk(t-1)、VktRelation of (A)k0And Ak1Is a constant; formula hkt＝hukt-hdktIndicating head hktEqual to the upstream water level huktAnd downstream water level hdktThe difference between them.
The objective function F is the minimum operation cost or the minimum energy consumption of the thermal power generating unit and the hydroelectric generating unit;
in particular, the objective function F may be
F＝min cTx+∑t∑idi(pit)+bTy
The method comprises the following steps that an objective function F is the minimum running cost of a thermal power generating unit and a hydroelectric generating unit, x is a decision variable of the starting and stopping state of the thermal power generating unit, y is a decision variable of the starting and stopping state of the hydroelectric generating unit, c is a starting and stopping price coefficient of the thermal power generating unit, b is a starting and stopping price coefficient of the hydroelectric generating unit, T represents a matrix transpose, diIs the running price coefficient, p, of the ith thermal power generating unititIs the output power, p, of the ith thermal power generating unitjtIs the output power of the jth hydroelectric generating set, pqtIs the q-th demand side load power,
the constraint conditions of the combined optimization scheduling model of the water-fire-electricity generator set are as follows:
Figure BDA0001231441200000081
Figure BDA0001231441200000082
Figure BDA0001231441200000083
Figure BDA0001231441200000084
wherein NT is the number of thermal power generating units, NH is the number of hydroelectric generating units, χ is the feasible set of the combined scheduling problem of the thermal power generating units, γ is the feasible set of the combined scheduling problem of the hydroelectric generating units, and Ω (x) is the feasible set of the economic scheduling of the thermal power generating units and is constrained by the capacity of a generator and the climbing output; psi (y) is a feasible set for economic dispatch of hydroelectric generating sets, pi is a scale factor of injection power, FlIs the maximum allowed transmission power of line l, equation ∑ipit+∑jpjt＝∑qpqtFor the electrical balance of the hair, formula-Fl≤∑iπilpit+∑jπjlpjt+∑qπqlpqt≤FlIs the limitation of the upper limit and the lower limit of the transmission power capacity.
Step 104: determining a water-fire-electricity generator set combined optimized dispatching model according to the single hydropower station optimized dispatching model which enables the objective function F to be minimum;
step 105: and scheduling the water-fire-electric generator set combination according to the model for optimizing the scheduling of the water-fire-electric generator set combination.
The above embodiment is an optimized scheduling method in a case of only a single hydropower station, and based on the optimized scheduling method, the optimized scheduling method for the combination of the hydroelectric generating sets is different from the above method in that the method further includes:
taking the output flow of the optimized dispatching model of the upper-level single hydropower station as the input flow of the optimized dispatching model of the current-level single hydropower station, and constructing an optimized dispatching model of the cascade hydropower station;
determining the constraint condition of the cascade hydropower station optimized scheduling model as the constraint condition of the single hydropower station optimized scheduling model of each stage;
carrying out piecewise linearization on the nonlinear constraint condition;
and solving the water-fire-electricity generator set combined optimization scheduling model which enables the objective function F to be minimum according to the linear constraint condition in the constraint conditions and the constraint condition after the piecewise linearization.
In the above embodiment, the construction of the hydroelectric model is considered from the practical physical meaning. Firstly, there is a reservoir scheduling concept in actual hydropower scheduling, namely: according to the result of runoff forecasting, the generating water consumption of each period is given after optimized dispatching, and the hydropower station distributes the water consumption of each time interval in the period under the condition of meeting safe and stable operation, so that the water consumption in the period is ensured to be used up. To simulate such a real physical process, the upper and lower layers are optimized using different time scales: in the upper layer, the total generating water consumption in a dispatching cycle is given by using a variable water head model and taking N hours as a unit; in the lower layer, the hydropower output of each time period is optimized under the limitation of the total water consumption (determined by the upper layer) in the period by utilizing a fixed water head model.
From the above analysis, the hydroelectric model has two nonlinear constraints, namely the relationship between the downstream water level and the power generation flow
Figure BDA0001231441200000091
Relation p between generated energy, generated flow and water headkt＝9.8hktqkt. The existence of the non-linear constraint brings the following difficulties to the solution of the robust scheduling problem: (1) due to the existence of nonlinear constraint, the problem is changed into a mixed integer non-convex nonlinear problem, the solution is difficult, and the solution speed is low; (2) the nonlinearity of the constraint leads to no guarantee of obtaining a globally optimal solution, and the robustness of the solution cannot be guaranteed. Aiming at the problem, the solution idea is as follows: the nonlinear constraints of hydropower are linearized. In the prior art, there is a mode of iterationThe nonlinear relationship can improve the accuracy of a small hydroelectric model, but the iteration times of a large-scale system can lead to the rapid increase of the calculation time.
Considering the non-linear relationship between the downstream water level and the power generation flow
Figure BDA0001231441200000101
Has polynomial form, can directly adopt a piecewise linearization method, and is a constraint condition formula for nonlinearity
Figure BDA0001231441200000102
The step of performing piecewise linearization may specifically include:
in the curve
Figure BDA0001231441200000103
Taking n points, and dividing the curve
Figure BDA0001231441200000104
Dividing the sub-field into n-1 sub-fields;
calculating function values corresponding to the n points;
determining a subsection where an independent variable is located, and determining function values of two end points of the subsection where the independent variable is located;
and determining the function value of the independent variable through the function values of the two endpoints, wherein the function value of the independent variable is a weighted average of the function values of the two endpoints.
Due to the nonlinear relation p between the generated energy and the generated flow and the water headt＝9.8htqtIt is complicated that the conventional way is to assume the reservoir head hktThe non-linear relationship is reduced to a linear relationship by a fixed value. This provides great convenience in solving the problem, but also introduces large errors. Aiming at the problem of low approximate precision, the existing solution is as follows: for small radial-flow hydropower stations, a first-order Taylor expansion method can be adopted, and errors caused by the method are small due to the small variation range of a water head and the power generation flow; for large groups of hydroelectric power stations, Taylor's are deployedThe method has larger error and needs to adopt a new thought. Considering that the difficulty of nonlinearity is that coupling of variables, substituting variables and separating variables can be performed, the constraint condition formula p for nonlinearity is used in the inventionkt＝9.8hktqktThe step of performing piecewise linearization specifically includes:
varying the water head hktThe calculation formula of (a) is substituted to obtain a relation having two polynomials, that is,
Figure 3
wherein p isktThe generated power of the kth reservoir at time t, qktThe generated flow of the kth reservoir at the time t, hktIs the difference between the water levels upstream and downstream of the kth reservoir at time t, huktUpstream level of the kth reservoir at time t, hdktIs the downstream water level, V, of the kth reservoir at time tktAmount of water at time t for the kth reservoir, Ak0、Ak1、Bk0、Bk1、Bk2、Bk3Is constant and can be adjusted according to specific practical conditions.
Respectively to two polynomials
Figure BDA0001231441200000111
And
Figure 4
respectively carrying out piecewise linearization;
taking n points on the curves of the two polynomials, and dividing each curve into n-1 subsections;
calculating function values corresponding to the n points;
determining a subsection where an independent variable is located through a variable from 0 to 1, and determining function values of two end points of the subsection where the independent variable is located;
and determining the function value of the independent variable through the function values of the two endpoints, wherein the function value of the independent variable is a weighted average of the function values of the two endpoints.
The output flow rate is approximated to the power generation flow rate in the model, and if the power generation loss water consumption is considered Ljt＝zjtSU, then the substitution of the above variables into a separate method introduces bilinear terms. But the water consumption of power generation and the starting and stopping state z of the hydroelectric generating setjtCorrelation, and the variable is a 0-1 variable (i.e. the variable value is 0 or 1), i.e. the feasible field of the bilinear term is discrete, so that the piecewise linearization method can still be adopted. Taking the term F ═ VtLjtFor example, the linearization is as follows:
F＝a1,j,tVminSU+a2,j,tVmaxSU
Vt＝a1,j,tVmin+a2,j,tVmax+a3,j,tVmin+a4,j,tVmax
a1,j,t+a2,j,t＝zjt,a3,j,t+a4,j,t＝1-zjt
the invention firstly combines the actual hydropower dispatching mode in China, provides a novel hydropower constraint linearization method, decouples and separates the hydropower variables, respectively linearizes the hydropower variables in a segmented manner, and avoids directly solving the non-convex nonlinear problem of nonlinear coupling of the variables in time and space. In addition, compared with the traditional methods such as constant head assumption, piecewise linearization on a plane and the like, the method has advantages in optimizing the total cost, the calculation time and the calculation precision in the treatment of the hydropower nonlinear constraint. Finally, the hydropower optimization scheduling model provided by the invention is a universal model, can be applied to a series of scheduling problems related to hydropower in an electric power system, combines the improved hydropower optimization scheduling model and a thermal power unit for combined scheduling, can effectively improve the operation efficiency of the electric power system, reduces the operation cost, and has practical significance.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (5)

1. A combined optimal scheduling method of a water-fire-electricity generating set based on mixed integer programming is characterized by comprising the following steps:
acquiring basic data of a single hydropower station, wherein the basic data comprises reservoir water quantity, a limiting value of the reservoir water quantity and a limiting value of power generation flow;
nonlinear constraint condition in constraint condition of optimal scheduling model of single hydropower station
Figure FDA0002278467920000011
And pkt＝9.8hktqktCarrying out piecewise linearization;
wherein k is the kth reservoir, t is time, formula
Figure FDA0002278467920000012
Indicating downstream water level hdktAnd the generated current qktRelation between Bk0、Bk1、Bk2And Bk3Is a constant; formula pkt＝9.8hktqktRepresenting the generated power p of a hydroelectric power stationktAnd the generated current qktWater head hktThe relationship between;
solving the single hydropower station optimized dispatching model which minimizes an objective function F according to the constraint condition after the piecewise linearization, the linear constraint condition of the single hydropower station optimized dispatching model and the basic data of the single hydropower station; the objective function F is the minimum operation cost or the minimum energy consumption of the thermal power generating unit and the hydroelectric generating unit;
the linear constraint conditions of the single hydropower station optimized dispatching model are as follows:
Figure FDA0002278467920000013
Figure FDA0002278467920000014
Figure FDA0002278467920000015
Figure FDA0002278467920000016
wherein k is the kth reservoir, t is time, formula Vk(t-1)-Vkt＝-Ikt+qktRepresenting the real-time balance of the water volume of the reservoir, i.e. the water volume V of the reservoir at time t-1k(t-1)Water volume V of reservoir at time tktThe difference is equal to the inflow IktAnd the generated current qktThe difference between the difference of the two phases,
Figure FDA0002278467920000017
the minimum value of the flow rate is shown,
Figure FDA0002278467920000018
the maximum flow is shown;
formula (II)
Figure FDA0002278467920000019
Indicating upstream water level huktWater volume V of reservoirk(t-1)、VktRelation of (A)k0And Ak1Is a constant; formula hkt＝hukt-hdktIndicating head hktEqual to the upstream water level huktAnd downstream water level hdktThe difference between the two;
determining a water-fire-electricity generator set combined optimized dispatching model according to the single hydropower station optimized dispatching model which enables the objective function F to be minimum;
and scheduling the water-fire-electric generator set combination according to the model for optimizing the scheduling of the water-fire-electric generator set combination.
2. The method of claim 1, wherein the objective function F is
F＝min cTx+∑t∑idi(pit)+bTy
The method comprises the following steps that an objective function F is the minimum running cost of a thermal power generating unit and a hydroelectric generating unit, x is a decision variable of the starting and stopping state of the thermal power generating unit, y is a decision variable of the starting and stopping state of the hydroelectric generating unit, c is a starting and stopping price coefficient of the thermal power generating unit, b is a starting and stopping price coefficient of the hydroelectric generating unit, T represents a matrix transpose, diIs the running price coefficient, p, of the ith thermal power generating unititIs the output power, p, of the ith thermal power generating unitjtIs the output power of the jth hydroelectric generating set, pqtIs the q-th demand side load power,
the constraint conditions of the combined optimization scheduling model of the water-fire-electricity generator set are as follows:
Figure FDA0002278467920000021
Figure FDA0002278467920000022
Figure FDA0002278467920000027
Figure FDA0002278467920000023
wherein NT is the number of thermal power generating units, NH is the number of hydroelectric generating units, χ is the feasible set of the combined scheduling problem of the thermal power generating units, γ is the feasible set of the combined scheduling problem of the hydroelectric generating units, and Ω (x) is the feasible set of the economic scheduling of the thermal power generating units and is constrained by the capacity of a generator and the climbing output; psi (y) is a feasible set for economic dispatch of hydroelectric generating sets, pi is a scale factor of injection power, FlIs the maximum allowed transmission of the line lPower, formula ∑ipit+∑jpjt＝∑qpqtFor the electrical balance of the hair, formula-Fl≤∑iπilpit+∑jπjlpjt+∑qπqlpqt≤FlIs the limitation of the upper limit and the lower limit of the transmission power capacity.
3. The method of claim 1, further comprising:
taking the output flow of the upper-level single hydropower station optimized scheduling model as the input flow of the current-level single hydropower station optimized scheduling model, and constructing a cascade hydropower station optimized scheduling model;
determining the constraint condition of the cascade hydropower station optimized dispatching model as the constraint condition of the single hydropower station optimized dispatching model of each stage;
carrying out piecewise linearization on the nonlinear constraint condition;
and solving the combined optimized dispatching model of the water-gas-electric generator set for minimizing the objective function F according to the constraint conditions and the constraint conditions after the piecewise linearization.
4. The method of claim 3, wherein the constraints of the scheduling model are optimized for a single hydropower station
Figure FDA0002278467920000024
Performing piecewise linearization, specifically comprising:
in the curve
Figure FDA0002278467920000025
Taking n points, and dividing the curve
Figure FDA0002278467920000026
Dividing the sub-field into n-1 sub-fields;
calculating function values corresponding to the n points;
determining a subsection where an independent variable is located, and determining function values of two end points of the subsection where the independent variable is located;
and determining the function value of the independent variable through the function values of the two endpoints, wherein the function value of the independent variable is a weighted average of the function values of the two endpoints.
5. The method of claim 1, wherein the nonlinear constraint p is a constraint on a single hydropower station optimization scheduling modelkt＝9.8hktqktPerforming piecewise linearization, specifically comprising:
varying the water head hktThe calculation formula of (a) is substituted to obtain a relation having two polynomials, that is,
Figure FDA0002278467920000031
respectively to two polynomials
Figure FDA0002278467920000032
And
Figure FDA0002278467920000033
respectively carrying out piecewise linearization;
taking n points on the curves of the two polynomials, and dividing each curve into n-1 subsections;
calculating function values corresponding to the n points;
determining a subsection where an independent variable is located, and determining function values of two end points of the subsection where the independent variable is located;
and determining the function value of the independent variable through the function values of the two endpoints, wherein the function value of the independent variable is a weighted average of the function values of the two endpoints.
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CN107844864A (en) * 2017-11-17 2018-03-27 大连理工大学 A kind of mixed-integer nonlinear programming model of Solving Hydropower Unit Commitment Problem
CN110619405B (en) * 2018-06-19 2022-12-02 新智数字科技有限公司 Method and device for optimizing mixed integer piecewise nonlinearity of universal energy station
CN109636015B (en) * 2018-11-28 2023-04-07 国网甘肃省电力公司电力科学研究院 Scheduling method for cascade hydropower virtual pumped storage power station
CN110110878B (en) * 2018-12-10 2022-11-18 国家电网有限公司西北分部 Day-ahead step hydropower combined optimization method and device
CN110909959B (en) * 2019-12-16 2023-04-07 国网青海省电力公司电力科学研究院 Wind power operation risk-considering multi-energy complementary power system robust optimization method
CN111130118B (en) * 2020-01-09 2021-02-02 清华大学 Optimal power flow calculation method of power system based on piecewise linearization

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102097866A (en) * 2011-03-28 2011-06-15 国电南瑞科技股份有限公司 Mid-long-term unit commitment optimizing method
CN103226735A (en) * 2013-03-26 2013-07-31 国家电网公司 Wind power segmentation-based electric power system optimal scheduling method
CN104901323A (en) * 2014-03-07 2015-09-09 国网上海市电力公司 Unit combination method in power system having RCAES
CN105322566A (en) * 2015-11-06 2016-02-10 山东大学 Wind power unit commitment contained modeling method considering predication error timing sequence distribution
CN106130079A (en) * 2016-07-08 2016-11-16 华北电力大学 A kind of edema due to wind pathogen fire short-term joint optimal operation method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102097866A (en) * 2011-03-28 2011-06-15 国电南瑞科技股份有限公司 Mid-long-term unit commitment optimizing method
CN103226735A (en) * 2013-03-26 2013-07-31 国家电网公司 Wind power segmentation-based electric power system optimal scheduling method
CN104901323A (en) * 2014-03-07 2015-09-09 国网上海市电力公司 Unit combination method in power system having RCAES
CN105322566A (en) * 2015-11-06 2016-02-10 山东大学 Wind power unit commitment contained modeling method considering predication error timing sequence distribution
CN106130079A (en) * 2016-07-08 2016-11-16 华北电力大学 A kind of edema due to wind pathogen fire short-term joint optimal operation method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Yiwei sun等.A new model for short-circuit current calculation of distribution networks integrated with numerous distributed generations .《2015 IEEE PES asia-pacific power and energy engineering conference(APPEEC)》.2015, *
基于混合整数线性规划的水电机组组合研究;刘勇;《万方在线公开：www.wanfangdata.com.cn/detail.do?_type=degree&id=D611998》;20150729;第3-4章 *
水火电联合短期调度的混合整数规划方法;吴宏宇等;《中国电机工程学报》;20091005;第29卷(第28期);第82-88页 *

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